
#    !pip install pandas
#    !pip install pandas-datareader
#    !pip install numpy
#    !pip install plotly_express
#    !pip install random


#import Libraries
import pandas as pd
from pandas_datareader import data as pdr
import numpy as np
import random
import plotly.graph_objects as go


# Define a function using Plotly Express
def plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    
    for i in df.columns[0:]:
        # Add range slider
        #fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df.index, y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
        #fig.update_traces(line_width = 3)
        fig.update_layout(legend=dict(orientation="h",)) 
        
    fig.show()
    
# Define a function using Plotly Express, changes axis y to logarithm scale
def log_plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    
    for i in df.columns[0:]:
        # Add range slider
        #fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df.index, y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
        #fig.update_traces(line_width = 3)
        fig.update_layout(legend=dict(orientation="h",)) 
    
    #changes y to logarithm scale
    fig.update_yaxes(type="log")
    fig.show()
    
# Define a function using Plotly Express, changes axis y to logarithm scale
def plotly_line(df, y, title):  
    
    # Create figure
    fig = go.Figure()
    fig.update_layout(title_text = title) 
    fig.add_scatter(x = df.index, y = y)
    # Update Layout
    fig.update_layout({'plot_bgcolor': "white"})
    #fig.update_traces(line_width = 3)
    fig.update_layout(legend=dict(orientation="h",)) 
    
    #changes y to logarithm scale
    fig.show()    
        
# Define a function using Plotly Express, changes axis y to logarithm scale
def log_plotly_line(df, y, title):  
    
    # Create figure
    fig = go.Figure()
    fig.update_layout(title_text = title) 
    fig.add_scatter(x = df.index, y = y)
    # Update Layout
    fig.update_layout({'plot_bgcolor': "white"})
    #fig.update_traces(line_width = 3)
    fig.update_layout(legend=dict(orientation="h",)) 
    
    #changes y to logarithm scale
    fig.update_yaxes(type="log")
    fig.show()


# Function to scale stock prices based on their initial starting price
# The objective of this function is to set all prices to start at a value of 1 
def price_scaling(raw_prices_df):
    scaled_prices_df = raw_prices_df.copy()
    for i in raw_prices_df.columns[0:]:
          scaled_prices_df[i] = raw_prices_df[i]/raw_prices_df[i][0]
    return scaled_prices_df


file_name = input('Input the CSV file name: ')
initial_investment = int(input('Input the initial investment: '))
n_runs = int(input('Input the number of simulations: '))

## BRL5
#      PETR4.SA VALE3.SA TAEE11.SA ITSA4.SA WEGE3.SA

## MAG7
#      AAPL AMZN GOOG META MSFT NVDA TSLA

## BRL_top10 
#      CRFB3.SA CPLE6.SA ECOR3.SA ITUB4.SA JBSS3.SA RENT3.SA PRIO3.SA SBSP3.SA VALE3.SA VIVT3.SA

Input the CSV file name: BRL5
Input the initial investment: 50000
Input the number of simulations: 10


#read CSV file
Stock_Prices_df = pd.read_csv(file_name)
#The code imports a DataFrame with num index [1,2,3...], this line replace the colum Date to Index
Stock_Prices_df.set_index(['Date'], inplace = True)


#obtain weights vector
n_assets = len(Stock_Prices_df.columns)
#lock vector to test function
weights = np.ones(n_assets) * 1/n_assets
weights

array([0.2, 0.2, 0.2, 0.2, 0.2])


#Define a function that performs an Asset Allocation
def asset_allocation(df, initial_investment, weights):
    ''' Performs an asset Allocation for a given DF, initial investment value and weights'''
    
    portfolio_df = df.copy()
    
    # Scale stock prices using the "price_scaling"
    scaled_df = price_scaling(df)
    
    #enumerate method links Stocks tickers in columns along with a counter position weight (i), like an index
    for i, stock in enumerate(scaled_df):
        portfolio_df[stock] = weights[i] * scaled_df[stock]  * initial_investment
    
    # Sum up all values and place the result in a new column titled "portfolio value [$]" 
    portfolio_df['Total Value [$]'] = portfolio_df.sum(axis = 1, numeric_only = True)
    
    # Calculate the portfolio percentage daily return and replace NaNs with zeros
    portfolio_df['Daily Return [%]'] = portfolio_df['Total Value [$]'].pct_change(1) * 100 
    portfolio_df.replace(np.nan, 0, inplace = True)
    
    return portfolio_df


#Asset Allocation with parameters defined
   
portfolio_df = asset_allocation(Stock_Prices_df, initial_investment, weights)
Eqw = portfolio_df
portfolio_df.round(2)


#Plot data:

plotly_line(portfolio_df, portfolio_df['Total Value [$]'], "Portfolio Total Value")
log_plotly_line(portfolio_df, portfolio_df['Total Value [$]'], "Portfolio Total Value - Log Scale")
plotly_line(portfolio_df, portfolio_df['Daily Return [%]'], "Daily Return [%]")


plotly_data(portfolio_df, "Equal Weighted Porfolio")
log_plotly_data(portfolio_df, "Equal Weighted Porfolio")


def rand_weights(n):
    ''' Produces n random weights that sum to 1 '''
    k = np.random.rand(n)
    return k / sum(k)


#obtain weights vector
n_assets = len(Stock_Prices_df.columns)

weights = rand_weights(n_assets).round(4)
display(weights)
sum(weights)

array([0.3312, 0.1094, 0.0768, 0.4263, 0.0563])

1.0


#Eqw     --  Equal Weighted </p>
#Rdw_1 -- Random Weighted 1 </p>
#Rdw_2 -- Random Weighted 2 [...]


def random_port_generate(initial_investment, n_runs):
    #obtain weights vector
    n_assets = len(Stock_Prices_df.columns)
    #lock vector to test function
    eq_weights = np.ones(n_assets) * 1/n_assets
    #Asset Allocation with parameters defined
    Eqw_df = asset_allocation(Stock_Prices_df, initial_investment, eq_weights)[['Total Value [$]', 'Daily Return [%]']]
    Eqw_df = Eqw_df.rename({'Total Value [$]':'Eqw [$]', 'Daily Return [%]':'Eqw [%]'}, axis="columns")
    All_df = Eqw_df
    
    # Placeholder to store all weights
    weights_runs = np.zeros((n_runs, n_assets))
    
    for i in range(n_runs):
        # Generate random weights 
        weights = rand_weights(n_assets)
        # Store the weights
        weights_runs[i,:] = weights
        # Random Asset Allocation
        df = asset_allocation(Stock_Prices_df, initial_investment, weights)[['Total Value [$]', 'Daily Return [%]']]
        #rename columns for iterate
        Rdw = df.rename({'Total Value [$]':'Rdw_{}[$]'.format(i), 'Daily Return [%]':'Rdw_{}[%]'.format(i)}, axis="columns")

        All_df = pd.merge(All_df, Rdw, on = 'Date')

        #All_df = Eqw_df.join(Rdw)

        print("Simulation Run = {}".format(i))   
        print("Weights = {}".format(weights_runs[i].round(3))) 
        print('\n')
        
    return All_df


df = random_port_generate(initial_investment, n_runs)
daily_returns_df = df.iloc[:, 1::2] 
total_values_df = df.iloc[:, 0::2] 
display(df.round(2))

Simulation Run = 0
Weights = [0.337 0.125 0.018 0.185 0.335]


Simulation Run = 1
Weights = [0.103 0.248 0.189 0.349 0.11 ]


Simulation Run = 2
Weights = [0.055 0.352 0.404 0.189 0.   ]


Simulation Run = 3
Weights = [0.002 0.289 0.308 0.108 0.293]


Simulation Run = 4
Weights = [0.249 0.202 0.185 0.221 0.142]


Simulation Run = 5
Weights = [0.129 0.007 0.146 0.353 0.366]


Simulation Run = 6
Weights = [0.397 0.244 0.285 0.063 0.012]


Simulation Run = 7
Weights = [0.088 0.171 0.325 0.231 0.185]


Simulation Run = 8
Weights = [0.194 0.433 0.029 0.123 0.222]


Simulation Run = 9
Weights = [0.22  0.239 0.236 0.134 0.171]


plotly_data(total_values_df, "Portfolios Total Value[$]")
log_plotly_data(total_values_df, "Portfolios Total Value[$]")
plotly_data(daily_returns_df, "Portfolio Daily Returns [%]")


import plotly.express as px
# Plot histograms for stocks daily returns using plotly express
fig = px.histogram(daily_returns_df)
fig.update_layout({'plot_bgcolor': "white"})


#Random Asset Allocation 01
weights_1 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_1))

Rdw_1 = asset_allocation(Stock_Prices_df, initial_investment, weights_1)
Rdw_1.round(2)

Value Invested: $ 50000
Number of Stocks: 5
Weights: [0.1258 0.238  0.294  0.1939 0.1483]


#Random Asset Allocation 02
weights_2 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_2))

Rdw_2 = asset_allocation(Stock_Prices_df, initial_investment, weights_2)
Rdw_2.round(2)

Value Invested: $ 50000
Number of Stocks: 5
Weights: [0.1903 0.0861 0.2853 0.1239 0.3144]


#Random Asset Allocation 03
weights_3 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_3))

Rdw_3 = asset_allocation(Stock_Prices_df, initial_investment, weights_3)
Rdw_3.round(2)

Value Invested: $ 50000
Number of Stocks: 5
Weights: [0.1544 0.191  0.3766 0.2723 0.0057]


#Random Asset Allocation 04
weights_4 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_4))

Rdw_4 = asset_allocation(Stock_Prices_df, initial_investment, weights_4)
Rdw_4.round(2)

Value Invested: $ 50000
Number of Stocks: 5
Weights: [0.282  0.0599 0.1778 0.2212 0.2591]


#Random Asset Allocation 02
weights_5 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_5))

Rdw_5 = asset_allocation(Stock_Prices_df, initial_investment, weights_5)
Rdw_5.round(2)

Value Invested: $ 50000
Number of Stocks: 5
Weights: [0.3105 0.2064 0.1784 0.0662 0.2384]


rand_value_df = pd.concat([  Eqw['Total Value [$]'], 
                           Rdw_1['Total Value [$]'], 
                           Rdw_2['Total Value [$]'], 
                           Rdw_3['Total Value [$]'], 
                           Rdw_4['Total Value [$]'], 
                           Rdw_5['Total Value [$]']
                           ], axis=1, join='inner').round(2)
#rand_value_df.to_csv('rand_value_df')


#read CSV file
rand_value_df = pd.read_csv('rand_value')
#The code imports a DataFrame with num index [1,2,3...], this line replace the colum Date to Index
rand_value_df.set_index(['Date'], inplace = True)

log_plotly_data(rand_value_df, "Total Value [$]")


rand_daily_df = pd.concat([  Eqw['Daily Return [%]'], 
                           Rdw_1['Daily Return [%]'], 
                           Rdw_2['Daily Return [%]'], 
                           Rdw_3['Daily Return [%]'], 
                           Rdw_4['Daily Return [%]'], 
                           Rdw_5['Daily Return [%]']
                           ], axis=1, join='inner').round(2)
#rand_daily_df.to_csv('rand_daily')


#read CSV file
rand_daily_df = pd.read_csv('rand_daily')
#The code imports a DataFrame with num index [1,2,3...], this line replace the colum Date to Index
rand_daily_df.set_index(['Date'], inplace = True)

plotly_data(rand_daily_df, "Total Value [$]")

	ITSA4.SA	PETR4.SA	TAEE11.SA	VALE3.SA	WEGE3.SA	Total Value [$]	Daily Return [%]
Date
2013-12-16	10000.00	10000.00	10000.00	10000.00	10000.00	50000.00	0.00
2013-12-17	10000.00	9688.58	9894.73	9965.11	9951.94	49500.36	-1.00
2013-12-18	10090.29	9711.65	9673.68	9994.18	9959.87	49429.68	-0.14
2013-12-19	10259.59	9948.10	9605.26	10209.36	10088.39	50110.70	1.38
2013-12-20	10056.43	9752.02	9684.21	10017.45	10024.13	49534.24	-1.15
...	...	...	...	...	...	...	...
2023-12-07	35384.54	60186.04	51144.70	38035.71	91657.85	276408.84	-0.11
2023-12-08	35857.31	62113.01	51329.54	38140.49	89817.44	277257.78	0.31
2023-12-11	35530.01	61878.89	50917.20	38171.93	90387.70	276885.73	-0.13
2023-12-12	35348.18	61374.64	51030.95	38240.03	90802.44	276796.24	-0.03
2023-12-13	36002.77	61824.86	51898.29	38198.12	92098.51	280022.55	1.17

	Eqw [$]	Eqw [%]	Rdw_0[$]	Rdw_0[%]	Rdw_1[$]	Rdw_1[%]	Rdw_2[$]	Rdw_2[%]	Rdw_3[$]	Rdw_3[%]	...	Rdw_5[$]	Rdw_5[%]	Rdw_6[$]	Rdw_6[%]	Rdw_7[$]	Rdw_7[%]	Rdw_8[$]	Rdw_8[%]	Rdw_9[$]	Rdw_9[%]
Date
2013-12-16	50000.00	0.00	50000.00	0.00	50000.00	0.00	50000.00	0.00	50000.00	0.00	...	50000.00	0.00	50000.00	0.00	50000.00	0.00	50000.00	0.00	50000.00	0.00
2013-12-17	49500.36	-1.00	49683.11	-0.63	49427.13	-1.15	49206.32	-1.59	49299.32	-1.40	...	49763.44	-0.47	49456.78	-1.09	49477.54	-1.04	49236.13	-1.53	49438.72	-1.12
2013-12-18	49429.68	-0.14	49870.52	0.38	49348.22	-0.16	48853.15	-0.72	49020.94	-0.56	...	49727.28	-0.07	49358.49	-0.20	49218.15	-0.52	49367.54	0.27	49331.48	-0.22
2013-12-19	50110.70	1.38	50712.38	1.69	50110.36	1.54	49381.60	1.08	49563.65	1.11	...	50409.17	1.37	49959.91	1.22	49750.55	1.08	50307.20	1.90	49974.20	1.30
2013-12-20	49534.24	-1.15	49968.87	-1.47	49466.75	-1.28	48958.29	-0.86	49201.80	-0.73	...	49873.08	-1.06	49366.62	-1.19	49341.05	-0.82	49509.14	-1.59	49425.59	-1.10
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-12-07	276408.84	-0.11	290550.40	0.12	258257.44	-0.12	254951.97	-0.40	320899.79	-0.31	...	296892.10	-0.03	233776.20	-0.11	278772.13	-0.26	296914.26	-0.04	275122.27	-0.15
2023-12-08	277257.78	0.31	289582.30	-0.33	260231.07	0.76	258943.44	1.57	321328.56	0.13	...	294212.70	-0.90	237248.40	1.49	279353.51	0.21	299591.39	0.90	276660.84	0.56
2023-12-11	276885.73	-0.13	289832.85	0.09	259750.93	-0.18	257640.22	-0.50	321205.75	-0.04	...	294792.58	0.20	235770.01	-0.62	278900.30	-0.16	299358.95	-0.08	276044.51	-0.22
2023-12-12	276796.24	-0.03	289979.03	0.05	259487.71	-0.10	256997.37	-0.25	321295.92	0.03	...	295620.35	0.28	235003.17	-0.33	279034.96	0.05	298610.38	-0.25	275775.75	-0.10
2023-12-13	280022.55	1.17	293571.84	1.24	261847.60	0.91	259680.70	1.04	325165.05	1.20	...	298987.33	1.14	238150.19	1.34	282267.38	1.16	301757.05	1.05	279137.01	1.22

	ITSA4.SA	PETR4.SA	TAEE11.SA	VALE3.SA	WEGE3.SA	Total Value [$]	Daily Return [%]
Date
2013-12-16	6290.00	11900.00	14700.00	9695.00	7415.00	50000.00	0.00
2013-12-17	6290.00	11529.41	14545.26	9661.17	7379.36	49405.20	-1.19
2013-12-18	6346.80	11556.86	14220.31	9689.36	7385.25	49198.58	-0.42
2013-12-19	6453.28	11838.23	14119.73	9897.98	7480.54	49789.77	1.20
2013-12-20	6325.50	11604.90	14235.79	9711.92	7432.90	49310.99	-0.96
...	...	...	...	...	...	...	...
2023-12-07	22256.88	71621.39	75182.71	36875.62	67964.29	273900.89	-0.23
2023-12-08	22554.25	73914.48	75454.42	36977.21	66599.63	275499.98	0.58
2023-12-11	22348.38	73635.88	74848.28	37007.68	67022.48	274862.70	-0.23
2023-12-12	22234.01	73035.82	75015.49	37073.71	67330.01	274689.04	-0.06
2023-12-13	22645.74	73571.59	76290.49	37033.08	68291.04	277831.94	1.14

	ITSA4.SA	PETR4.SA	TAEE11.SA	VALE3.SA	WEGE3.SA	Total Value [$]	Daily Return [%]
Date
2013-12-16	9515.00	4305.00	14265.00	6195.00	15720.00	50000.00	0.00
2013-12-17	9515.00	4170.93	14114.84	6173.38	15644.45	49618.60	-0.76
2013-12-18	9600.92	4180.87	13799.51	6191.40	15656.92	49429.60	-0.38
2013-12-19	9762.00	4282.66	13701.90	6324.70	15858.95	49930.21	1.01
2013-12-20	9568.69	4198.24	13814.52	6205.81	15757.94	49545.21	-0.77
...	...	...	...	...	...	...	...
2023-12-07	33668.39	25910.09	72957.91	23563.12	144086.14	300185.66	-0.17
2023-12-08	34118.23	26739.65	73221.59	23628.04	141193.01	298900.51	-0.43
2023-12-11	33806.81	26638.86	72633.39	23647.51	142089.46	298816.02	-0.03
2023-12-12	33633.79	26421.78	72795.65	23689.70	142741.43	299282.36	0.16
2023-12-13	34256.64	26615.60	74032.91	23663.74	144778.85	303347.74	1.36

	ITSA4.SA	PETR4.SA	TAEE11.SA	VALE3.SA	WEGE3.SA	Total Value [$]	Daily Return [%]
Date
2013-12-16	7720.00	9550.00	18830.00	13615.00	285.00	50000.00	0.00
2013-12-17	7720.00	9252.59	18631.79	13567.49	283.63	49455.50	-1.09
2013-12-18	7789.71	9274.63	18215.54	13607.08	283.86	49170.81	-0.58
2013-12-19	7920.40	9500.43	18086.71	13900.05	287.52	49695.11	1.07
2013-12-20	7763.57	9313.18	18235.37	13638.76	285.69	49236.55	-0.92
...	...	...	...	...	...	...	...
2023-12-07	27316.87	57477.67	96305.47	51785.62	2612.25	235497.87	-0.28
2023-12-08	27681.84	59317.92	96653.52	51928.28	2559.80	238141.36	1.12
2023-12-11	27429.17	59094.34	95877.09	51971.08	2576.05	236947.72	-0.50
2023-12-12	27288.79	58612.78	96091.27	52063.80	2587.87	236644.52	-0.13
2023-12-13	27794.14	59042.74	97724.48	52006.75	2624.81	239192.92	1.08

#006 Asset Allocation with Random Portfolio Weights¶

libs¶

Functions¶

6.1 Equal Weighted Portfolio¶

6.2 Random Weighted Portfolio¶

6.2.1 Define a function to generate random weights¶

6.2.2 Asset Allocation Auto¶

6.2.2.1 Plotting Data¶

6.2.3 Asset Allocation Manual¶

6.2.3.1 Organize and Plot Data¶

	ITSA4.SA	PETR4.SA	TAEE11.SA	VALE3.SA	WEGE3.SA	Total Value [$]	Daily Return [%]
Date
2013-12-16	14100.00	2995.00	8890.00	11060.00	12955.00	50000.00	0.00
2013-12-17	14100.00	2901.73	8796.42	11021.41	12892.74	49712.29	-0.58
2013-12-18	14227.32	2908.64	8599.90	11053.57	12903.01	49692.44	-0.04
2013-12-19	14466.02	2979.45	8539.08	11291.55	13069.51	50345.62	1.31
2013-12-20	14179.57	2920.73	8609.26	11079.30	12986.27	49775.12	-1.13
...	...	...	...	...	...	...	...
2023-12-07	49892.21	18025.72	45467.64	42067.49	118742.74	274195.80	-0.01
2023-12-08	50558.80	18602.85	45631.96	42183.39	116358.49	273335.48	-0.31
2023-12-11	50097.32	18532.73	45265.39	42218.15	117097.26	273210.85	-0.05
2023-12-12	49840.93	18381.70	45366.51	42293.48	117634.56	273517.19	0.11
2023-12-13	50763.91	18516.55	46137.58	42247.12	119313.61	276978.78	1.27

	ITSA4.SA	PETR4.SA	TAEE11.SA	VALE3.SA	WEGE3.SA	Total Value [$]	Daily Return [%]
Date
2013-12-16	15525.00	10320.00	8920.00	3310.00	11920.00	49995.00	0.00
2013-12-17	15525.00	9998.61	8826.10	3298.45	11862.71	49510.88	-0.97
2013-12-18	15665.18	10022.42	8628.92	3308.07	11872.17	49496.77	-0.03
2013-12-19	15928.01	10266.44	8567.89	3379.30	12025.36	50167.00	1.35
2013-12-20	15612.61	10064.08	8638.31	3315.78	11948.77	49579.55	-1.17
...	...	...	...	...	...	...	...
2023-12-07	54934.50	62111.99	45621.07	12589.82	109256.15	284513.54	-0.06
2023-12-08	55668.47	64100.62	45785.95	12624.50	107062.38	285241.93	0.26
2023-12-11	55160.34	63859.01	45418.14	12634.91	107742.14	284814.54	-0.15
2023-12-12	54878.05	63338.63	45519.60	12657.45	108236.51	284630.24	-0.06
2023-12-13	55894.31	63803.26	46293.27	12643.58	109781.42	288415.84	1.33